Computer system and method for generating healthcare risk indices using medication compliance information

ABSTRACT

A healthcare risk index is generated using a patient or individual&#39;s pharmacy claims. The index may be used to explain and predict variation in pharmacy-related costs and variation in total healthcare costs or utilization. In particular, the index is generated by first examining the individual&#39;s pharmacy claims to identify any chronic conditions possessed by the individual. Similarly, the individual&#39;s pharmacy claims are examined to identify any compliance medications prescribed to the individual. The chronic condition information is used to generate a chronic condition score by summing regression coefficients for each chronic condition possessed by the individual. Likewise, the compliance medication information is used to generate a compliance medication score by summing products of regression coefficients for each compliance medication prescribed to the individual with associated medication supply weights. From there, a modified chronic condition score is generated by multiplying the chronic condition score by an overall chronic condition regression coefficient. The modified chronic condition score may then be further modified by subtracting a no-claims weight from the chronic condition score in cases where the individual has no pharmacy claims. Finally, the risk index may be determined by summing the modified chronic condition score and the compliance medication score.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed to computer-related and/or assistedsystems, methods, and computer program devices for facilitatingefficient and effective healthcare management programs. Moreparticularly, the present invention relates to techniques for generatinga risk index which may be used for clinical case identification, such ase.g., disease management programs, to explain and predict variation inpharmacy-related costs, and to explain and predict variation in totalhealthcare costs or utilization.

2. Description of the Related Art

A major economic problem that has surfaced during the past twenty yearshas been the upward spiraling cost of medical care. Demographic factorshave played one role in this increased cost since extended lifeexpectancies increase the percentage of older individuals in thepopulation. Generally, such individuals require a much higher degree ofmedical care.

A second major factor contributing to increased costs for medical carehas been the advent of many new, expensive, medical procedures whichhave sprung from medical and instrumentation advances of the past tenyears. More widely known examples are organ transplants and the use ofCAT scanners or MRI units for routine diagnosis.

An additional factor resulting in these increased costs has been theincreased rate of inflation, which has dramatically influenced the costsfor drugs. Due to all of the above, as well as other factors, the costof even routine medical care has increased dramatically.

Correspondingly, increasing numbers of healthcare studies have beencommissioned with the stated goal of optimizing healthcare services andexpenditures. For instance, numerous methods and techniques have beenproposed, which attempt to increase healthcare efficiency by predictinghealthcare costs.

For example, U.S. Pat. No. 4,667,292, issued to Mohlenbrock, et al., in1987, and incorporated herein by reference, discloses a medicalreimbursement computer system which generates a list identifying themost appropriate diagnostic-related group (DRG) and related categoriesapplicable to a given patient for inpatient claims (see, e.g., STEPS33-65 of Prior Art FIG. 11). The list is limited by a combination of thecharacteristics of the patient and an initial principal diagnosis. Aphysician can choose a new designation from a list of related categorieswhile the patient is still being treated. Manually determined ICD-9numbers can then be applied to an available grouper computer program tocompare the working DRG to the government's DRG. This information may beused in conjunction with predicting healthcare costs.

U.S. Pat. No. 5,018,067, also issued to Mohlenbrock, et al., in 1991,and incorporated herein by reference, discloses an apparatus and methodfor improved estimation of healthcare resource consumption through theuse of diagnostic and/or procedure-grouping and severity of illnessindicators. This system is a computer-implemented program thatcalculates the amount of payment to a health provider by extracting thesame input data as that identified in the Mohlenbrock '292 Patent (whichdiscloses the DRG System). The system calculates the severity of thepatient's illness then classifies each patient into sub-categories ofresource consumption within a designated DRG. A computer combines theinput data according to a formula consisting of constants and variables.The variables are known for each patient and relate to the number of ICDcodes and the government weighing of the codes. The software programdetermines a set of constants for use in the formula for a given DRGwhich minimizes variances between the actual known outcomes and thoseestimated by use of the formula. Because it is based upon various levelsof illness severity within each diagnosis, the results of this systemprovide a much more homogenous grouping of patients than is provided bythe DRGs. Providers can be compared to identify those providers whosepractice patterns are of the highest quality and most cost efficient. Aset of actual costs incurred can be compared with the estimated costs.After the initial diagnosis, the system determines the expected costs oftreating a patient.

U.S. Pat. No. 5,325,293 to Dorne, issued in 1994, and incorporatedherein by reference, discloses a system and method for correlatingmedical procedures and medical billing codes. After an examination, thesystem automatically determines raw codes directly associated with allof the medical procedures performed or planned to be performed with aparticular patient. The system allows the physician to modify theprocedures after performing the examination. By manipulating the rawcodes, the system generates intermediate and billing codes withoutaltering the raw codes.

While useful in their own ways, the techniques disclosed in theabove-described prior art references, however, fail to meet all of theneeds of today's healthcare community. For example, it has beendetermined by the inventors of the present invention that each of thetechniques described above fail to consider the predictive nature ofpharmacy claims-based data (e.g., the ability to predict and explainvariation in costs using claims data). As a result, the prior artmethods fail to address situations where patients refill prescriptionswithout visiting a physician's office (e.g., where patients refillprescriptions based on a number of refills given with an initialprescription).

Thus, none of the techniques described above, make use of pharmacyclaims data to predict, e.g., healthcare costs. More particularly, ithas been determined by the inventors of the present invention that theseand other prior art techniques fail to consider the predictive qualitiespossessed by a patient's pharmacy claims. Furthermore, these and otherprior art techniques fail to consider the predictive nature of apatient's compliance with specific pharmaceuticals.

What is therefore needed is a technique that predicts risk based onchronic conditions possessed by an individual patient as determinedaccording to the individual's pharmacy claims information.

Furthermore, it has been determined by the inventors of the presentinvention that a need also exists for a technique that predicts riskbased on an individual patient's compliance with instructions providedon those medications.

SUMMARY OF THE INVENTION

The present invention is directed to generating a healthcare risk indexusing a patient's or individual's pharmacy claims, which are indicativeof, for example, chronic conditions possessed by the individual, theindividual's compliance on certain medications, and situations where theindividual has no pharmacy claims whatsoever. The index may be used toexplain and predict variation in pharmacy-related costs and variation intotal healthcare costs or utilization.

Various consideratations and/or factors may be used in creating thehealthcare risk index. One example of the method used to create thehealthcare index includes first examining the individual's pharmacyclaims to identify any chronic conditions possessed by that individual.Similarly, the individual's pharmacy claims are examined to identify anycompliance medications prescribed to the individual. The chroniccondition information is used to generate a chronic condition score bysumming regression coefficients for each chronic condition possessed bythe individual. Likewise, the compliance medication information is usedto generate a compliance medication score by summing products ofregression coefficients for each compliance medication prescribed to theindividual with associated medication supply weights. From there, amodified chronic condition score is generated by multiplying the chroniccondition score by an overall chronic condition regression coefficient.The modified chronic condition score may then be further modified bysubtracting a no-claims weight from the chronic condition score in caseswhere the individual has no pharmacy claims. Finally, the risk index maybe determined by summing the modified chronic condition score and thecompliance medication score. Any variation of the above method mayalternatively be used that considers similar, additional and/or otherfactors in determining the healthcare risk index.

One embodiment of the present invention is now summarized. Inparticular, a compliance-based risk index is generated using pharmacyclaims to estimate risk. More specifically, the compliance-based riskindex represents a pharmacy claims-based co-morbidity risk index. Theindex was developed to allow accurate comparisons between variouspopulations by adjusting for a “burden of illness.” In addition, theindex may be used to predict future medical costs, total healthcarecosts, and probability and amounts of future medical servicesutilization.

In use, individual patients (e.g., members of a particular healthcareinsurance plan) receive risk scores based on chronic medications used,as well as their compliance on those medications. Scores increase, forexample, with the number of diseases present, with more costly diseasesreceiving higher scores. In addition, plan members with non-chronicacute medication use are distinguished from those with no utilization.In one embodiment, the risk index was developed using patientinformation from a conventional pharmacy claims database and frompatient eligibility data. In other embodiments, other pharmacy claimsdatabases, along with patient eligibility information, may be utilizedin conjunction with the present invention. For example, databaseinformation provided by any health insurer or pharmacy benefits managermay just as easily be utilized. In any event, scoring is based on valuesobtained from these data sources.

The uses of such a compliance-based risk index are many. For example,the index may be used for research and actuarial purposes, such asclinical case identification uses (e.g., disease management programs).Similarly, the index may be used to explain and predict variation inpharmacy-related costs and variation in total healthcare costs orutilization. Further, the index may be used as a tool in programevaluation to create comparable groups to adjust for factors such asadverse or favorable selection into healthplans, programs orhealth-related interventions.

Thus, the compliance-based risk index of the present inventionadvantageously solves, for example, three problems: clinical caseidentification/disease management, prediction of concurrent andprospective pharmacy-related and total healthcare costs, and allows thecomparison of groups which may have differing rates of chronic illness.

The probability sample, which in one embodiment is a pharmacy claimsdatabase, was used to develop the index. More particularly, the pharmacyclaims from the data source are first reviewed to determine whichconditions exist for each patient and indicator variables are set if theconditions exist. Sample chronic conditions indicator weights areprovided in Table I (shown below).

TABLE I Category Chronic Conditions Weights XA Acid Peptic Disorders10.3039 XB Treatment for acne 10.6623 XC Attention Deficit/Hyperactivity13.9197 Disorder (under 18) XD Advanced Liver Disease 0.54999 XE AIDS31.9221 XF Allergic Rhinitis 10.1509 XG Amyotrophic Lateral Schlerosis(Lou 29.0981 Gehrig's disease) XH Alzheimer's Disease 6.54956 XIAngina/Coronary Artery Disease 0.067019 XJ Anxiety Disorder/PanicDisorder/ 3.17593 Social Phobia XK Arrythmia 6.09765 XL Asthma (under55) 9.0274 XM Benign Prostatic Hyperplasia 7.97429 XN Cancer (any type)10.8211 XO Congestive Heart Failure 2.18044 XP Chronic ObstructivePulmonary 3.57019 Disease (55+) XQ Cystic Fibrosis 5.16423 XR Depression10.3768 XS Diabetes Type I - Insulin dependent 9.019 XT Diabetes TypeII - Non-Insulin 7.29497 dependent XU End Stage Renal Disease (ESRD)12.5402 XV Epilepsy 6.93106 XW Gaucher's Disease 83.2477 XX Glaucoma2.31705 XY Gout 1.98149 XZ Growth Hormone Deficiency 32.512 XAAHepatitis B 4.09929 XBB Hepatitis C 33.2874 XCC HighCholesterol/Triglycerides 10.7383 XDD Hypertension 8.30913 XEEHypothyroidism 1.21422 XFF Inflammatory Bowel Disease 8.56743 XGG ManicDepressive 4.62228 XHH Migraine 8.26695 XLL Multiple Sclerosis 30.5853XMM Organ Transplantation 17.0153 XNN Menopause (Hormone Replacement7.26419 Therapy) (45-59) XOO1 Osteoporosis (Bone Resorption 6.69366Suppression Agents) (60+) XOO2 Osteoporosis (Estrogenic Agents) 3.50463(60+) XPP Parkinson's Disease 8.11787 XQQ Peripheral Vascular Disease1.76236 XRR Psoriasis 9.92265 XSS Psychotic Disorders/Dementia and4.5396 no antidepressants (65+) XTT Rheumatoid Arthritis 10.5781 XUUSchizophrenia (under 65) 13.8768 XVV Smoking Cessation 3.40704 XWWThromboembolytic Disease I 1.44029 (Platelet Aggregation Inhibitors) XKKThromboembolytic Disease II (Oral 2.83761 Anticoagulants, Coumarin Type)XYY Tuberculosis 5.049 XZZ Urinary Incontinence 3.66661

The weights are then, for example, multiplied by the indicator variables(e.g., “1” for “TRUE” or the presence of chronic condition and “0” for“FALSE” or the absence of the chronic condition) and, for example,summed to get a chronic conditions score. Furthermore, an indicator forpatients with no pharmacy claims (e.g., a “no-claim” weight) mayoptionally be considered (set to a value of 1 for members with nopharmacy claims and 0 otherwise, which is multiplied by a no-claimsweight) to further modify the chronic conditions score. Thus, thisno-claim weight further emphasizes situations where an individual has nopharmacy claims whatsoever (i.e., the patient has no chronic conditionclaims, prescribed medications and/or other claims).

The pharmacy claims are also reviewed to determine compliance on certainpharmaceuticals of interest. In one embodiment, compliance is defined asthe total days of supply over a year (e.g., the days supply divided by365 times 100%). In other embodiments, other time periods are used(e.g., 7 days, 30 days, etc.). Two sets of weights, one from a logtransform (or log index) and one from a square-root transform (orsquare-root index) are included in Table II (shown below), for eachmedication, for use in generating a compliance medication score. Eitherweight may be used. When the weights are developed in practice, in somecases, whichever transform minimizes the multiple regression model'serror sum of squares may be most appropriate. The weights are multipliedby the indicator or numeric variables (i.e., the days supply orcompliance) and summed to generate the medication compliance score.Table II, below, provides exemplary indices (the generation of which isdescribed in greater detail below) for a number of compliancemedications.

TABLE II Weights for log Weights for square root Compliance Medicationindex index Chronic Condition Score 0.08036 0.65876 No Claim −2.24249−5.42877 Asthma Medications 0.00734 0.07566 Asthma Controllers 0.002460.06420 Congestive Heart Failure −0.00095396 0.00615 MedicationsAngiotension Converting 0.00183 0.00987 Enzymes GastrointestinalMedications −0.00041974 0.04681 Proton Pump Inhibitors 0.00490 0.07727High Cholesterol Medications 0.00020414 0.01141 Statins 0.00339 0.04491Diabetes Medications −0.00010360 0.07623 Diabetes Type 2 Medications0.00285 0.06342 Depression Medications 0.00358 0.07158 HypertensionMedications 0.00811 0.04302

Once generated, the medical compliance score and the chronic conditionsscore are summed to produce the risk index of the present invention,which (as mentioned above) may be used to predict future medical costs,total healthcare costs, and probability and amounts of future medicalservices utilization. Specifically, an individual with a higher scorewill likely have more comorbidities, and hence represent a moreexpensive patient, than an individual with a lower score. Thus, the riskindices of the individuals in a particular population may be compared,with higher scores indicating a high risk of pharmaceutical cost andrisk of future total medical cost and medical utilization.

In at least one embodiment, the index is generated by first examiningthe individual's pharmacy claims to identify any predeterminedconditions, such as chronic conditions possessed by the individual.Similarly, the individual's pharmacy claims are examined to identify anycompliance medications prescribed to the individual. The chroniccondition information is used, for example, to generate a chroniccondition score by summing regression coefficients for each chroniccondition possessed by the individual. Likewise, the compliancemedication information is used, for example, to generate a compliancemedication score by, for example, summing products of regressioncoefficients for each compliance medication prescribed to the individualwith associated medication supply weights. From there, a modifiedchronic condition score is generated by, for example, multiplying thechronic condition score by a chronic condition regression coefficient.The modified chronic condition score may then be further modified by,for example, subtracting a no-claims weight from the chronic conditionscore in cases where the individual has no pharmacy claims. Finally, therisk index may be determined by, for example, summing the modifiedchronic condition score and the compliance medication score.

Other embodiments of the present invention also provide a method,system, and computer-readable instructions for generating a risk indexfor an individual using the individuals pharmacy claims. In thesealternate embodiments, the index is generated by first generating a rawrisk index indicative of at least one of the individual's raw relativemedical costs, acute medical conditions, and variation in medical costs,by using the individual's pharmacy claims. Next, the raw risk index ismodified in accordance with a compliance medication score, which isindicative of the individual's compliance with prescribedpharmaceuticals. Summing the above values results in the risk index ofthis embodiment of the present invention.

In yet other cases, other embodiments of the present invention providemethod, system, and computer-readable instructions for generating a riskindex for an individual using the individual's pharmacy claims and ano-claims weight. In these alternate embodiments, the index is generatedby first generating a raw risk index indicative of at least one of theindividual's raw relative medical costs, acute medical conditions, andvariation in medical costs, by using the individual's pharmacy claims.Subsequently, the raw risk index is modified in accordance with ano-claims weight, which is indicative of an absence of claims in theindividual's pharmacy claims. Like the above, summing these valuesresults in the risk index of this embodiment of the present invention.

There has thus been outlined, rather broadly, the more importantfeatures of the invention in order that the detailed description thereofthat follows may be better understood, and in order that the presentcontribution to the art may be better appreciated. There are, of course,additional features of the invention that will be described hereinafterand which will form the subject matter of the claims appended hereto.

In this respect, before explaining at least one embodiment of theinvention in detail, it is to be understood that the invention is notlimited in its application to the details of construction and to thearrangements of the components set forth in the following description orillustrated in the drawings. The invention is capable of otherembodiments and of being practiced and carried out in various ways.Also, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting.

As such, those skilled in the art will appreciate that the conception,upon which this disclosure is based, may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

These together with other objects of the invention, along with thevarious features of novelty which characterize the invention, arepointed out with particularity in the claims annexed to and forming apart of this disclosure. For a better understanding of the invention,its operating advantages and the specific objects attained by its uses,reference should be had to the accompanying drawings and descriptivematter in which there is illustrated preferred embodiments of theinvention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

The above-mentioned and other advantages and features of the presentinvention will be better understood from the following detaileddescription of the invention with reference to the accompanyingdrawings, in which:

FIG. 1 is one example of a process utilizable for generating a chroniccondition score and a compliance medication score for use in generatinga risk index according to the techniques of the present invention;

FIG. 2 is one example of a portion of a process utilizable forgenerating a risk index according to the techniques of the presentinvention;

FIG. 3 illustrates one example of the generation of the risk index ofthe present invention without a compliance medication component;

FIG. 4 illustrates one example of the generation of the risk index ofthe present invention with a compliance medication component;

FIG. 5 is an example of another embodiment of a process utilizable forgenerating a risk index according to the techniques of the presentinvention;

FIG. 6 lists examples of chronic conditions and associated regressioncoefficients utilizable in generating the risk index of the presentinvention;

FIG. 7 lists examples of compliance medications and associatedregression coefficients utilizable in generating the risk index of thepresent invention;

FIG. 8 is an example of yet another embodiment of a process utilizablefor generating a risk index according to the techniques of the presentinvention;

FIG. 9 is a block diagram example of a computer utilizable forgenerating the risk index of the present invention;

FIG. 10 illustrates a block diagram of the internal hardware of thecomputer of FIG. 9; and

FIG. 11 depicts a prior art method used to implement a medicalreimbursement computer program.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description includes many specific details. Theinclusion of such details is for the purpose of illustration only andshould not be understood to limit the invention. Throughout thisdiscussion, similar elements are referred to by similar numbers in thevarious figures for ease of reference. In addition, features in oneembodiment may be combined with features in other embodiments of theinvention.

Specifically, a healthcare risk index is generated using, for example,information from a patient or individual's pharmacy claims, which areindicative of, for example, chronic conditions possessed by theindividual, the individual's compliance on certain medications, andsituations where the individual has no claims whatsoever. The index maybe used to explain and predict variation in pharmacy-related costs andvariation in total healthcare costs or utilization. In particular, theindex is generated, for example, by first examining the individual'spharmacy claims to identify any chronic conditions possessed by theindividual. Similarly, the individual's pharmacy claims are examined toidentify, for example, any compliance medications prescribed to theindividual. The chronic condition information is used, for example, togenerate a chronic condition score by summing regression coefficientsfor each chronic condition possessed by the individual. Likewise, thecompliance medication information is used, for example, to generate acompliance medication score by summing products of regressioncoefficients for each compliance medication prescribed to the individualwith associated medication supply weights. From there, a modifiedchronic condition score is generated by, for example, multiplying thechronic condition score by a chronic condition regression coefficient.The modified chronic condition score may then be further modified by,for example, subtracting a no-claims weight from the chronic conditionscore in cases where the individual has no pharmacy claims. Finally, therisk index may be determined, for example, by summing the modifiedchronic condition score and the compliance medication score.

Referring first to FIG. 1, one example of a process utilizable forgenerating a chronic condition score and a compliance medication scoreused to generate a risk index according to the techniques of the presentinvention is illustrated. As will be discussed in greater detail below,these scores may be utilized to generate a risk score or risk index ofthe present invention. More specifically, the risk index of the presentinvention represents a “burden of illness” score, which when compared toother scores, may be used to predict relative healthcare costs. In atleast some embodiments, higher scores imply more comorbidities, andhence represent more expensive relative illnesses. The index allows foraccurate comparisons between various populations by adjusting for a“burden of illness.” In addition, the index may be used to predictfuture medical costs, total healthcare costs, and probability andamounts of future medical services utilization.

In accordance with the concepts of the present invention, numerousfactors may affect the risk index. Specifically, the scores may increaseaccording to the number of diseases present, but generally not accordingto the number of prescriptions within a category. Similarly, more costlydiseases may receive higher scores. In at least some embodiments of thepresent invention, the index specifically considers a patient'scompliance with certain medications, as well as situations where apatient has no pharmacy claims whatsoever (i.e., the patient received noprescriptions).

The generation of the risk index commences (STEP 104) with thedetermination of an eligible population for a specified period of time(STEP 108). To perform this step, the process may examine the contentsof an eligibility file. In most cases, the eligibility file includes adatabase containing a list of each of the eligible individuals,maintained by a managed care organization, a pharmacy benefits manager,a human resources department of an employer, or other similarorganization. For example, the eligible individuals may include each ofthose employees of a particular employer that have elected to receivehealthcare benefits from the employer. In contrast, individuals thathave elected not to receive healthcare benefits from the employerconstitute ineligible individuals.

Thus, the process examines each entry or individual for eligibility(STEP 112). For each ineligible individual, processing terminates (STEP116), with no risk score being generated for that individual. For thosethat are eligible, pharmacy claims information for the individuals isexamined (STEP 120).

Specifically, a pharmacy file, which is used to maintain the medicalhistory data for each of the individuals, is retrieved and subsequentlyconsulted. In addition to the patient's name, gender, age, and otherpersonal information, this file includes information describing eachprescription filled under a benefit offer by the employer. This includespharmaceuticals filled from outpatient pharmacies, retail claims, mailorder prescription claims, or other similar claims, but excludesmedications sold over-the-counter. Hence, the information in thepharmacy file necessarily identifies, for example, a chronic conditionpossessed by each individual (if any), as well as the individuals'compliance with the medication, which may be defined as the number ofdays supply of medication coverage for the time period divided by thenumber of days in the time period multiplied by 100%, and the like. Anytime periods are possible, include, for example, 365 days, 7 days, etc.

Once the pharmacy claims information for the eligible individuals hasbeen retrieved, the data for each individual is reviewed (STEP 124).Specifically, the process first identifies whether each individual hasany pharmacy claims. As part of this procedure, the process identifiesthose individuals that have no pharmacy claims whatsoever.

In accordance with the concepts of the present invention, individualswith no claims are at the lowest risk for adverse events and utilizationfor costs. As a result, the scores associated with these individuals aremodified in accordance with a “no-claims” weight or modifier (discussedin greater detail below). Thus, in the example illustrated in FIG. 1,no-claims indicators associated with these individuals are set (STEP128). For example, an indicator may be set to “1” or “TRUE” if theindividual has no claims. Correspondingly, compliance variables forthese individuals (i.e., indicators relating to a patient's compliancewith medications) are set to indicate that there was no use ofmedications.

As discussed above, with individuals determined to have at least oneclaim (as determined in STEP 124), the no claims indicator is set (STEP132). For example, an indicator may be set to “0” or “FALSE” if theindividual has no claims. Subsequently, the pharmacy claims data foreach of these individuals is examined to identify the presence orabsence of a chronic condition of interest (STEP 136).

More specifically, the pharmacy claims are searched for prescribedmedications used to treat any of a predetermined number of chronicconditions or diseases. In the example illustrated in FIG. 1, thechronic conditions listed in FIG. 6 may be utilized. Thus, the processin FIG. 1 identifies the presence (or absence) of each of those chronicconditions for each individual. Furthermore, although specific chronicconditions are provided in FIG. 6, it should be noted that theseconditions are listed for exemplary purposes only, and that numerousother conditions may just as easily be considered. Specifically, theexact combination of chronic conditions may be determined by theadministrator or user of the system (see e.g., the embodiment of FIG.5).

If the process determines that an individual possesses one of thepredetermined chronic conditions of interest, an indicator correspondingto that condition is set to “1” or to a “TRUE” state (STEP 152). If, onthe other hand, the process determines that the individual does notpossess the predetermined chronic condition of interest, the indicatorcorresponding to that condition is set to “0” or to a “FALSE” state(STEP 140).

In addition to the presence or the absence of a chronic condition, thepresent invention also considers a patient or individual's compliance onthe medications used to treat certain chronic conditions. Morespecifically, an individual's compliance measures whether that patient'sillness is being treated properly. As will be discussed in greaterdetail below, one embodiment of the present invention measurescompliance according to the amount or supply of a medication prescribedto a patient (for a specified period of time). Thus, whereas a chroniccondition score is used to measure the presence or absence of certainchronic conditions, a compliance medication score is used to measure anindividual's compliance with a medication.

To illustrate, asthma is a measurable chronic condition. As such itspresence (or absence) may be measured using an asthma chronic conditionscore. In addition, asthma may typically be treated using asthmacontroller medications. Compliance with this medication may thereforealso be measured, specifically, using an asthma medication compliancescore. The present invention considers these factors because, forexample, an asthmatic patient who does not use his or her medicationproperly may be at a higher risk for an adverse event than a patient whois asthmatic but has a high compliance with controller medications (andis therefore at a lower risk).

Thus, the process searches the pharmacy claims for the presence of anyconditions where medication compliance is to be considered (STEP 156).In the example illustrated in FIG. 1, the medications listed in FIG. 7may be utilized. Furthermore, although specific medications are providedin FIG. 7, it should be noted that these medications are listed forexemplary purposes only, and that numerous other medications may just aseasily be considered. Specifically, the exact combination of compliancemedications may be determined by the administrator or user of the system(see e.g., the embodiment of FIG. 5).

Once the compliance medications have been determined (STEP 156), acompliance medication score for each medication is generated (STEP 160).In the example illustrated in FIG. 1, this score is generated by summingthe days supply for each medication, dividing by a predetermined numberof days (e.g., 365 days, 1 week, etc.), and multiplying by 100%.

Thus, the above described process continues with an analysis of eachpharmacy claim of interest (STEP 136) and an analysis of any desiredconditions for medication compliance (STEP 156) until each claim hasbeen examined (STEP 144). From there, the above-generated information isutilized to generate a chronic condition score (STEP 148). Specifically,for each chronic condition possessed by an individual, a weightassociated with the condition is added to the chronic condition score.As an example, the weights listed in the table of FIG. 6 may be used.Thus, a chronic condition score associated with a patient diagnosed withcancer is increased by the weight associated with cancer, which in thiscase is 10.8211. Similarly, if that same patient also smokes, thechronic condition score is increased by 3.40704. The sum total of thesechronic condition weights thereby constitutes the chronic conditionscore of an individual (or i.e., a raw chronic condition score inembodiments where modifications or the absence of pharmacy claims areconsidered).

The weights associated with the chronic conditions are generated usinginformation contained in a pharmacy claims database. Specifically, eachweight represents the regression coefficient developed using a multipleregression model with pharmaceutical or total cost as the dependentvariable and all chronic conditions of interest as independentvariables. The coefficients shown in the example of FIG. 6 weredetermined using data from a pharmaceutical benefits manager database(one example of which includes Medco Health Solutions pharmacy claimsdatabase). Other data sources (such as other health insurers, PBMscontaining pharmacy claims, etc.) may just as easily be utilized bythose of ordinary skill in the art to calculate these regressioncoefficients. After generating the chronic condition score, a risk indexis calculated (STEP 150).

Referring now to FIG. 2, one example of a portion of a processutilizable for generating a risk index is now described. As mentionedabove, the risk index of the present invention advantageouslyincorporates any number of factors including, for example, the numberand types of chronic conditions and diseases, modifications forvariance, modifications for patients with no pharmacy claims, and thelevel of compliance with any number of prescribed medications.

The risk index calculation starts (STEP 204) by initializing the index,in this example, to zero (STEP 208). As discussed above, the index maybe adjusted according to the number and types of chronic conditionspossessed by an individual. As discussed above with reference to STEP148 of FIG. 1, for each chronic condition possessed, the risk index maybe modified by a weight associated with the condition.

Once the chronic condition weights have been summed, the total mayoptionally be modified according to a regression coefficient relating tothe entire or overall chronic condition score (STEP 212). In the examplein FIG. 2, the sum of the chronic condition weights is multiplied by anoverall regression coefficient.

In accordance with the concepts of the present invention, a modificationmay also be made for patients with no pharmaceutical claims (STEP 216).Specifically, in the example of FIG. 2, the indices associated withindividuals with no pharmaceutical claims whatsoever are modifiedaccording to a no-claim weight. For instance, as depicted in FIG. 2, theregression coefficient for the no-claim indicator (e.g., 2.24249) issubtracted from the chronic condition score. An example of a no-claimsweight calculated using data from a pharmacy claims database (oneexample of which includes Medco Health Solutions' pharmacy claimsdatabase) is shown in FIG. 7.

Although the example of FIG. 2 shows the no claim weight beingsubtracted from the chronic condition score, it is to be understood thatmodifications (including the no claim weight and others) may be made toany component of the risk index and at any time during the process. Forexample, the no claim weight may just as easily be applied as a fractionto be multiplied against the compliance medication score (describedbelow).

In addition to the chronic condition score and other modifiers (e.g.,the no-claim and variance modifiers), a compliance medication componentis also considered in the generation of the risk index. Specifically,each medication to be considered for compliance is associated with aregression coefficient for that medication (see the examples listed inFIG. 7). To generate the compliance medication score, the number of dayssupply divided by the number of days in the time period multiplied by100% (i.e., a compliance) for each medication is multiplied against thecompliance medication's regression coefficient.

Thus, in the example of FIG. 2, the compliance for asthma medications ismultiplied by 0.00734 (STEP 220); the compliance for asthma controllersis multiplied by 0.00246 (STEP 224); and the compliance for hypertensionmedications is multiplied by 0.00811 (STEP 232). Other compliancemedications of interest are addressed in a similar manner (STEP 228).

Once these individual compliance medication scores have been generated,they are summed and added to the risk index, thereby completing the riskindex generation process (STEP 236).

Although specific regression coefficients were provided in the exampleof FIG. 2 above (i.e., based on data obtained from, e.g., the MedcoHealth Solutions pharmacy claims database), it is to be understood thatthese values were provided for exemplary purposes only. Specifically,those of ordinary skill in the art will recognize that each distinctdata source would result in the generation of alternative regressioncoefficients.

FIG. 3 illustrates one example of the generation of a portion of therisk index of the present invention without a compliance medicationcomponent. In this example, processing starts (STEP 304) with anidentification of all chronic conditions of interest from, e.g., apharmacy claims database. Here, the process identifies the existence ofGaucher's Disease. Thus, the process sets a Gaucher's Disease indicatorto 1 or TRUE (STEP 308). This indicator is then multiplied by theregression coefficient for Gaucher's Disease (i.e., 83.2477 in theexample of FIG. 6) to produce a weight (e.g., 83.2477), which is addedto the patient's risk index.

FIG. 4 illustrates one example of the generation of a portion of therisk index of the present invention with a compliance medicationcomponent. The process commences (STEP 404) with an identification ofall chronic conditions of interest from, e.g., a pharmacy claimsdatabase. Here, the process identifies the existence of asthma. Thus,the process sets an asthma indicator to 1 or TRUE (STEP 408). Thisindicator is multiplied by the regression coefficient for asthma (i.e.,9.0274 in the example of FIG. 6) to produce a weight (e.g., 9.0274),which is added to the patient's risk index.

Referring again to FIG. 4, because the medications used to treat asthmaare also of interest in this example, the process continues by checkingthe patient's compliance with asthma medications and controllers. Thus,the process sums the days supply for asthma medications other thancontrollers (STEP 412) while at the same time summing the days supplyfor asthma controllers (STEP 416). These values are converted tocompliance measures by dividing by the number of days in the time periodand multiplying by 100%. These compliance measures are later multipliedby the regression coefficient for asthma medications (other thancontrollers) (i.e., 0.0734 in the example of FIG. 7) and by theregression coefficient for asthma controllers (i.e., 0.00246 in theexample of FIG. 7), respectively. Assuming a time period of 365 days anda 150 day supply of asthma medication for an asthma medicationcompliance of 41% (i.e., 150/365×100%) and a 365 day supply of asthmacontrollers for an asthma controller compliance of 100%, the risk indexis increased by 0.0326 (i.e., 41%×0.0734+100%×0.00246).

FIG. 5 is an example of another embodiment of a process utilizable forgenerating the risk index of the present invention. The process starts(STEP 504) with an identification of the chronic conditions to beconsidered in generating the present risk index (STEP 508). For example,a query may be made to a process administrator or user for the chronicconditions of interest. Any chronic conditions may be utilized inaddition to the conditions listed in FIG. 6.

Once the chronic conditions of interest have been determined, thecompliance medications of interest are determined (STEP 512). Like withthe identification of chronic conditions, a user may be asked to inputthe compliance medications of interest. Similarly, the information maybe downloaded from a file or other data source.

Once the conditions and medications of interest have been identified, aneligible population for a specified period of time is determined (STEP514). To perform this step, the process may examine a databasemaintained by a managed care organization, a pharmacy benefits manager,a human resources department of an employer, or other similarorganization, and identify each of the individuals that are eligible forparticipation in a benefits plan (i.e., individuals that have elected toreceive healthcare benefits from an employer).

For those individuals that are eligible, regression coefficients fortheir chronic conditions and compliance medications are calculated. Tocalculate the regression coefficients, as known to those of ordinaryskill in the art, a multiple regression model is run using the pharmacyor total cost for each patient as the dependent variable and includingan independent variable for each condition and medication of interestfor each patient. The model is run on the data using statisticalsoftware which outputs the estimates of the regression coefficientsbased on the fit of the model to the data. For example, the regressioncoefficients for each of the chronic conditions of interest may becalculated by running a multiple regression model using pharmacy ortotal costs for each patient as the dependent variable and including anindependent variable for each condition of interest for each patient.The model is run on the data using statistical software which outputsthe estimates of the regression coefficients based on the fit of themodel to the data (STEP 516). Similarly, the regression coefficients foreach of the compliance medications of interest may be calculated byrunning a multiple regression model using pharmacy or total cost foreach patient as the dependent variable and including an independentvariable for each compliance medication of interest from apharmaceutical benefits manager database (such as e.g., the Medco HealthSolutions database) for each patient. The model is run on the data usingstatistical software which outputs the estimates of the regressioncoefficients based on the fit of the model to the data (STEP 520).

Subsequently, a raw or unmodified chronic condition score may begenerated by summing the regression coefficients of each of the chronicconditions possessed by the individuals or patients (STEP 524). Asdiscussed above, the presence or absence of a condition may bedetermined by examining the pharmacy claims for each individual. Thisprocess continues until each individual has been checked for eachchronic condition (STEP 532).

In accordance with the concepts of the present invention, the rawchronic condition score may be modified according to a regressioncoefficient relating to the entire or overall chronic condition score(STEP 540). Similarly, a no-claim modification may be made to accountfor individuals that possess no pharmacy claims whatsoever (STEP 544).More particularly, a no-claims weight may be subtracted from the score.These modifications may be made to produce a modified or final chroniccondition score.

In conjunction with the calculation of the chronic condition score, acompliance medication score may be generated by multiplying thecompliance of prescribed medication against an associated regressioncoefficient for each of the compliance medications possessed by theindividuals or patients (STEP 528). As discussed above, the compliancemedications prescribed to an individual, and the compliance thereof, maybe determined by examining the pharmacy claims for each individual. Thisprocess continues until each individual has been checked for eachcompliance medication (STEP 536).

From there, the modified chronic condition score may be added to thecompliance medication score to result in the risk index of the presentinvention (STEP 548). The above process continues, repeating STEPS 524,528, 532, 536, 540, 544, and 548 for the eligible individuals of thepopulation (STEP 550), until a risk index has been generated for each(STEP 552).

Furthermore, although each of the examples above describes the use ofpharmacy claims information in the generation of the risk index of thepresent invention, it is to be understood that factors in addition to orin place of pharmacy claims information may be utilized. For example,alternate embodiments of the present invention contemplate usinganalogous and/or other similar information such as medical informationin place thereof.

FIG. 8 is an example of yet another embodiment of a process utilizablefor generating the risk index of the present invention. The processstarts (STEP 1004) with an identification of any health conditions to beconsidered in generating the present risk index (STEP 1008). As anexample, this may include any chronic conditions of interest, althoughother conditions are possible.

Once the health conditions have been determined, the medications ofinterest are identified (STEP 1012). As an example, this may include anycompliance medications of interest, although other medications arepossible.

From there, medical information for each individual is examined tocalculate regression coefficients for each health condition andmedication associated with each individual. For example, the methodsdescribed above (e.g., the procedures described in FIGS. 1-5 above) maybe used. Subsequently, these results may be summed (STEP 1016) togenerate a raw score for each individual (STEP 1020).

Once the raw scores have been generated for each individual, they mayoptionally be modified (STEP 1024) to consider, for example, individualswith no claims and/or other factors (see, e.g., STEPS 540 and 544 ofFIG. 5 above), to result in the risk index of the present invention.

The risk index generation process of the present invention may beimplemented in any computer system or computer-based controller. Oneexample of such a system is described in greater detail below withreference to FIG. 9. More specifically, FIG. 9 is an illustration of acomputer 58 used for implementing the computer processing in accordancewith a computer-implemented embodiment of the present invention. Theprocedures described above may be presented in terms of programprocedures executed on, for example, a computer or network of computers,including local and/or global area networks such as the Internet.

Viewed externally in FIG. 9, computer 58 has a central processing unit(CPU) 68 having disk drives 69, 70. Disk drives 69, 70 are merelysymbolic of a number of disk drives that might be accommodated bycomputer 58. Typically, these might be one or more of the following: afloppy disk drive 69, a hard disk drive (not shown), and a CD ROM ordigital video disk, as indicated by the slot at 70. The number and typeof drives varies, typically with different computer configurations. Diskdrives 69, 70 are, in fact, options, and for space considerations, maybe omitted from the computer system used in conjunction with theprocesses described herein.

Computer 58 also has a display 71 upon which information may bedisplayed. The display is optional for the computer used in conjunctionwith the system described herein. A keyboard 72 and/or a pointing device73, such as a mouse 73, may be provided as input devices to interfacewith central processing unit 68. To increase input efficiency, keyboard72 may be supplemented or replaced with a scanner, card reader, or otherdata input device. The pointing device 73 may be a mouse, touch padcontrol device, track ball device, or any other type of pointing device.

Alternatively, referring to FIG. 10, computer 58 may also include a CDROM reader and writer 80, which are interconnected by a bus 74 alongwith other peripheral devices supported by the bus structure andprotocol. Bus 74 serves as the main information highway interconnectingother components of the computer.

FIG. 10 illustrates a block diagram of the internal hardware of thecomputer of FIG. 9. CPU 75 is the central processing unit of the system,performing calculations and logic operations required to execute aprogram. Read only memory (ROM) 76 and random access memory (RAM) 77constitute the main memory of the computer. Disk controller 78interfaces one or more disk drives to the system bus 74. These diskdrives may be floppy disk drives such as 79, or CD ROM or DVD (digitalvideo/versatile disk) drives, as at 80, or internal or external harddrives 81. As previously indicated these various disk drives and diskcontrollers are optional devices.

A display interface 82 permits information from bus 74 to be displayedon the display 83. Again, as indicated, the display 83 is an optionalaccessory for a central or remote computer in the communication network,as are infrared receiver 88 and transmitter 89. Communication withexternal devices occurs using communications port 84.

In addition to the standard components of the computer, the computer mayalso include an interface 85, which allows for data input through thekeyboard 86 or pointing device, such as a mouse 87.

The foregoing detailed description includes many specific details. Theinclusion of such detail is for the purpose of illustration only andshould not be understood to limit the invention. In addition, featuresin one embodiment may be combined with features in other embodiments ofthe invention. Various changes may be made without departing from thescope of the invention as defined in the following claims.

As another example, the system according to the invention may include ageneral purpose computer, or a specially programmed special purposecomputer. The user may interact with the system via e.g., a personalcomputer or over PDA, e.g., the Internet an Intranet, etc. Either ofthese may be implemented as a distributed computer system rather than asingle computer. Similarly, the communications link may be a dedicatedlink, a modem over a POTS line, and/or any other method of communicatingbetween computers and/or users. Moreover, the processing could becontrolled by a software program on one or more computer systems orprocessors, or could even be partially or wholly implemented inhardware.

Although the computer system in FIG. 9 is illustrated as having a singlecomputer, the system according to one or more embodiments of theinvention is optionally suitably equipped with a multitude orcombination of processors or storage devices. For example, the computermay be replaced by, or combined with, any suitable processing systemoperative in accordance with the concepts of embodiments of the presentinvention, including sophisticated calculators, hand held,laptop/notebook, mini, mainframe and super computers, as well asprocessing system network combinations of the same. Further, portions ofthe system may be provided in any appropriate electronic format,including, for example, provided over a communication line as electronicsignals, provided on floppy disk, provided on CD Rom, provided onoptical disk memory, etc.

Any presently available or future developed computer software languageand/or hardware components can be employed in such embodiments of thepresent invention. For example, at least some of the functionalitymentioned above could be implemented using Visual Basic, C, C++ or anyassembly language appropriate in view of the processor being used. Itcould also be written in an interpretive environment such as Java andtransported to multiple destinations to various users.

The many features and advantages of the embodiments of the presentinvention are apparent from the detail specification, and thus, it isintended by the appended claims to cover all such features andadvantages of the invention that fall within the true spirit and scopeof the invention. Further, since numerous modifications and variationswere readily occurred to those skilled in the art, it is not desired tolimit the invention to the exact construction and operation illustratedand described, and accordingly, all suitable modifications andequivalents maybe resorted to, falling within the scope of theinvention.

1. A computer implemented method for generating a chronic condition riskindex for an individual using said individual's pharmacy claims by acomputer system, said method comprising the steps of: (1) retrieving bythe computer system said pharmacy claims from a pharmacy claimsdatabase, and processing by the computer system said pharmacy claims toidentify any chronic conditions possessed by said individual; (2)processing by the computer system said pharmacy claims to identify anycompliance medications prescribed to said individual; (3) generating achronic condition score by summing regression coefficients for eachchronic condition possessed by said individual; (4) generating acompliance medication score by summing products of regressioncoefficients for each compliance medication prescribed to saidindividual with associated medication supply weights, or compliance; (5)generating a modified chronic condition score by multiplying saidchronic condition score by an overall chronic condition regressioncoefficient; (6) further modifying said modified chronic condition scoreby subtracting a no-claims weight from said chronic condition score, ifsaid individual has no pharmacy claims; and (7) generating, by thecomputer system, said chronic condition risk index by summing saidmodified chronic condition score and said compliance medication score.2. The method of claim 1, wherein said associated medication supplyweights are determined by summing a total supply of an associatedcompliance medication prescribed to said individual.
 3. The method ofclaim 1, wherein said compliance medication score represents saidindividual's compliance for an associated compliance medication.
 4. Themethod of claim 1, wherein said no-claims weight indicates that saidindividual has not been diagnosed with a chronic condition.
 5. Themethod of claim 1, wherein said no-claims weight indicates that saidindividual has not been prescribed any medications whatsoever.
 6. Themethod of claim 1, wherein said no-claims weight is not applied if saidindividual has been prescribed a medication.
 7. The method of claim 1,wherein said overall chronic condition regression coefficient accountsfor variances in said chronic condition risk index.
 8. The method ofclaim 1, wherein said pharmacy claims are obtained from the pharmacyclaims database managed by at least one of a managed care organization,a pharmacy benefits manager, or a human resources department.
 9. Themethod of claim 1, further comprising the step of retrieving, by thecomputer system, patient information comprising patient eligibility datafrom at least one of a health insurer database and a pharmacy benefitsdatabase, and wherein said generating, by the computer system, saidchronic condition risk index further comprises generating the chroniccondition risk index responsive to the patient eligibility data.
 10. Themethod of claim 1, further comprising the step of determining, by thecomputer system, using the chronic condition risk index clinical caseidentification with respect to the patient.
 11. The method of claim 1,further comprising the step of determining, by the computer system,using the chronic condition risk index, and determining variation in atleast one of estimated total healthcare costs and utilization by thepatient.
 12. The method of claim 1, further comprising the step ofdetermining, by the computer system, using the chronic condition riskindex, whether to generate comparable groups which may have differingrates of chronic illness and adjusting for factors including at leastone of adverse and favorable selection into at least one health plans,programs and health-related interventions.
 13. A computer system forgenerating a chronic condition risk index for an individual using saidindividual's pharmacy claims, said computer system comprising processorand a memory medium storing instructions for controlling said processor,wherein said processor executes the instructions stored on said memorymedium and performs the following functionality: (1) retrieving by thecomputer system said pharmacy claims from a pharmacy claims database,and processing by the computer system said pharmacy claims to identifyany chronic conditions possessed by said individual; (2) processing bythe computer system said pharmacy claims to identify any compliancemedications prescribed to said individual; (3) generating a chroniccondition score by summing regression coefficients for each chroniccondition possessed by said individual; (4) generating a compliancemedication score by summing products of regression coefficients for eachcompliance medication prescribed to said individual with associatedmedication supply weights, or compliance; (5) generating a modifiedchronic condition score by multiplying said chronic condition score byan overall chronic condition regression coefficient; (6) furthermodifying said modified chronic condition score by subtracting ano-claims weight from said chronic condition score, if said individualhas no pharmacy claims; and (7) generating, by the computer system, saidchronic condition risk index by summing said modified chronic conditionscore and said compliance medication score.
 14. The computer system ofclaim 13, wherein said associated medication supply weights aredetermined by summing a total supply of an associated compliancemedication prescribed to said individual.
 15. The computer system ofclaim 13, wherein said compliance medication score represents saidindividual's compliance for an associated compliance medication.
 16. Thecomputer system of claim 13, wherein said no-claims weight indicatesthat said individual has not been diagnosed with a chronic condition.17. The computer system of claim 13, wherein said no-claims weightindicates that said individual has not been prescribed any medicationswhatsoever.
 18. The computer system of claim 13, wherein said no-claimsweight is not applied if said individual has been prescribed amedication.
 19. The computer system of claim 13, wherein said overallchronic condition regression coefficient accounts for variances in saidchronic condition risk index.
 20. The computer system of claim 13,wherein said pharmacy claims are obtained from the pharmacy claimsdatabase managed by at least one of a managed care organization, apharmacy benefits manager, or a human resources department.
 21. Thecomputer system of claim 13, wherein said computer system retrieves saidpatient information comprising patient eligibility data from at leastone of a health insurer database and a pharmacy benefits database, andwherein said generating, by the computer system, said chronic conditionrisk index further comprises generating the chronic condition risk indexresponsive to the patient eligibility data.
 22. The computer system ofclaim 13, wherein said computer system determines using the chroniccondition risk index clinical case identification with respect to thepatient.
 23. The computer system of claim 13, wherein said computersystem determines using the chronic condition risk index, anddetermining variation in at least one of estimated total healthcarecosts and utilization by the patient.
 24. The computer system of claim13, wherein said computer system determines using the chronic conditionrisk index whether to generate comparable groups which may havediffering rates of chronic illness and adjusts for factors including atleast one of adverse and favorable selection into at least one healthplans, programs and health-related interventions.
 25. The method ofclaim 1, wherein if said individual has no pharmacy claims, an indicatorrelating to said patient's compliance with a prescribed medication isset to indicate that there was no use of said prescribed medication. 26.The computer system of claim 13, wherein if said individual has nopharmacy claims, an indicator relating to said patient's compliance witha prescribed medication is set to indicate that there was no use of saidprescribed medication.
 27. The computer implemented method of claim 1,wherein said generated chronic condition risk index is used in at leastone of explaining and predicting variation in pharmacy-related costs andvariation in total healthcare costs or utilization.
 28. The computersystem of claim 13, wherein said generated chronic condition risk indexis used in at least one of explaining and predicting variation inpharmacy-related costs and variation in total healthcare costs orutilization.
 29. The computer implemented method of claim 1, furthercomprising determining at least one of pharmacy-related costs and totalhealthcare costs using said generated chronic condition risk index. 30.The computer system of claim 13, further comprising determining at leastone of pharmacy-related costs and total healthcare costs using saidgenerated chronic condition risk index.